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This work examines the possibility of a rapid formation of glauconite in a relatively shallow platform environment (below fair-weather wave baseline). The materials studied here are uppermost Jurassic alternations of carbonate beds and marly interbeds, namely, the Assises de Croï Formation of the Boulonnais area (northernmost France). The carbonate beds yield field evidences of an early diagenetic origin and both beds and interbeds contain glauconite, questioning the duration of formation of the glauconite, relative to that of the diagenetic carbonate beds. Carbon and oxygen stable isotope composition of the carbonate beds confirm an early diagenetic growth. Contrasted grain-size distribution patterns of glauconite and quartz grain populations (isolated after acid digestion and magnetic separation) evidenced that glauconite formed after sediment deposition. Glauconite formation allegedly requires protracted episodes of ion capture from the water column, which is no longer possible when glauconite gets trapped within authigenic carbonates. Therefore, in-situ glauconite formation preceded carbonate authigenesis. Yet, the chemical composition of grains (Fe and K) typifies glauconite as highly evolved, meaning that its formation must have lasted over times, according to conventional views. Consequently, our results challenge these conventional views and confirm that glauconite can form in relatively shallow environments (which has been already brought to light previously) and it is concluded that early diagenetic glauconite can be markedly enriched in both K and Fe, which is an unprecedented result.

Glauconite is an authigenic mineral reputed to form during long-lasting contact between a nucleus (a pre-existing phyllosilicate) and seawater. This protracted contact makes it possible to subtract the ions necessary for the construction of the neoformed phyllosilicate, here, glauconite (a mineral very close to an illite, rich in K and Fe). As a result, glauconite is often associated with sediments deposited in a transgressive context with a strong slowdown in the rate of sedimentation and a relatively large water layer thickness. This is the case of the Cenomanian chalk of Boulonnais (north of France). Being chemically and physically resistant, glauconite is a mineral that is often reworked, like quartz grains. This is frequently the case of the Jurassic deposits of the Boulonnais, where glauconite, almost ubiquitous, either in traces or in significant proportions of the sediments, presents a grain size sorting attesting to its transport and reworking. However, these Jurassic deposits are shallow (shoreface, upper offshore), which supports the idea that the “glauconite factory” was itself in the shallow areas of the Boulonnais. The only identified Jurassic facies of the Boulonnais where glauconite is both relatively abundant, large in size and unsorted (non reworked) are oyster reefs that formed at the outlet of cold seeps linked to a late-Jurassic synsedimentary tectonic (Kimmeridgian, Tithonian). Our work makes it possible to hypothesize that isolated oyster reefs were environments combining the redox conditions and in contact with seawater favoring the authigenic formation of glauconite. The weakly reducing conditions necessary for the formation of glauconite here are attested by the contents of metallic trace elements sensitive to redox conditions (vanadium, germanium, arsenic, in this case). Our work thus adds a new element to the understanding of the mechanisms of formation of glauconite in shallow environments.

Background The therapeutic strategy for mycetoma relies heavily on the identification of the causative agents, which are either fungal or bacterial. While histopathological examination of surgical biopsies is currently the most used diagnostic tool, it requires well-trained pathologists, who are lacking in most rural areas where mycetoma is endemic. In this work we propose and evaluate a machine learning approach that semi-automatically analyses histopathological microscopic images of grains and provides a classification of the disease as eumycetoma or actinomycetoma. Methods The computational model is based on radiomics and partial least squares. It is assessed on a dataset that includes 890 individual grains collected from 168 patients originating from the Mycetoma Research Centre in Sudan. The dataset contained 94 eumycetoma cases and 74 actinomycetoma cases, with a distribution of the species among the two causative agents that is representative of the Sudanese distribution. Results The proposed model achieved identification of causative agents with an accuracy of 91.89%, which is comparable to the accuracy of experts from the domain. The method was found to be robust to a small error in the segmentation of the grain and to changes in the acquisition protocol. Among the radiomics features, the homogeneity of mycetoma grain textures was found to be the most discriminative feature for causative agent identification. Conclusion The results presented in this study support that this computational approach could greatly benefit rural areas with limited access to specialized clinical centres and also provide a second opinion for expert pathologists to implement the appropriate therapeutic strategy. [ABSTRACT FROM AUTHOR]

Though the morphological discrimination of the three pseudo-cryptic Ammonia species, A. aberdoveyensis, A. confertitesta and A. veneta, has been recently established, information on their ecology and habitats are still relatively scarce. This study aims to define distribution patterns of these species at eight sites scattered along the French coasts of the English Channel, over a total of 39 stations. These sites were classified into two habitats, either harbours (heavily modified sites) or less impacted (moderately influenced sites). The use of IndVal index (an index based on how a species is statistically specific to a habitat) clearly indicates that A. confertitesta is recorded preferentially in or close to harbours. Considering its non-indigenous species (NIS) status in Europe, we investigated its reported occurrences in Europe in the literature. It almost always showed a proximity to major European harbours. Sometimes, this species occurred relatively far away from these harbours, suggesting a secondary spread. Finally, this work interprets A. confertitesta being a NIS in the eastern English Channel with assumptions of being invasive regarding its dominance over the indigenous species A. aberdoveyensis and A. veneta. Complementary works such as retrospective core studies of fossil faunas are needed to quantitatively assess when and where A. confertitesta was introduced in Europe and potentially started to replace its congenerics A. veneta and A. aberdoveyensis. [ABSTRACT FROM AUTHOR]

Background: The therapeutic strategy for mycetoma relies heavily on the identification of the causative agents, which are either fungal or bacterial. While histopathological examination of surgical biopsies is currently the most used diagnostic tool, it requires well-trained pathologists, who are lacking in most rural areas where mycetoma is endemic. In this work we propose and evaluate a machine learning approach that semi-automatically analyses histopathological microscopic images of grains and provides a classification of the disease as eumycetoma or actinomycetoma., Methods: The computational model is based on radiomics and partial least squares. It is assessed on a dataset that includes 890 individual grains collected from 168 patients originating from the Mycetoma Research Centre in Sudan. The dataset contained 94 eumycetoma cases and 74 actinomycetoma cases, with a distribution of the species among the two causative agents that is representative of the Sudanese distribution., Results: The proposed model achieved identification of causative agents with an accuracy of 91.89%, which is comparable to the accuracy of experts from the domain. The method was found to be robust to a small error in the segmentation of the grain and to changes in the acquisition protocol. Among the radiomics features, the homogeneity of mycetoma grain textures was found to be the most discriminative feature for causative agent identification., Conclusion: The results presented in this study support that this computational approach could greatly benefit rural areas with limited access to specialized clinical centres and also provide a second opinion for expert pathologists to implement the appropriate therapeutic strategy., (© The Author(s) 2023. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.)